1. Field of the Invention
The present invention generally relates to three-dimensional (3D) depth generation, and more particularly to 3D depth generation by local blurriness estimation.
2. Description of the Prior Art
When three-dimensional (3D) objects are mapped onto a two-dimensional (2D) image plane by prospective projection, such as an image taken by a still camera or video captured by a video camera, a lot of information, such as the 3D depth information, disappears because of this non-unique many-to-one transformation. That is, an image point cannot uniquely determine its depth. Recapture or generation of the 3D depth information is thus a challenging task that is crucial in recovering a full, or at least an approximate, 3D representation, which may be used in image enhancement, image restoration or image synthesis, and ultimately in image display.
A still or video camera as mentioned above typically includes, among other things, a lens, through which incoming parallel rays pass to converge and intersect at a focal point along a lens optical axis. The distance from the lens to the focal point is called the focal length. An object in the 2D image is in focus (or focused) if light from the object is, or is almost, converged, and is out of focus (or de-focus, defocused, or unfocused) if light from the object is not well converged. The de-focus or defocused object in the image appears blurred, and the blur degree, or blurriness, is in proportion to the distance or the depth. Therefore, a measure of the degree of de-focus is conventionally used to generate 3D depth information.
One conventional 3D depth information generation method is performed by collecting the blurriness of a specific area which is captured many times respectively with different distances. Hence, 3D depth information of the entire image can be obtained according to the collected blurriness and distances.
Another conventional 3D depth information generation method is performed by applying a 2D frequency transform or a high pass filter to respective areas of a single image in order to determine magnitude(s) of high-frequency component(s), representing respective blurriness. Hence, 3D depth information of an entire image can be obtained according to the blurriness. This method unfortunately fails when objects in the image have different colors, have the same, close or similar brightness, or have indistinct texture, as a result of difficulties being presented in differentiating the blurriness among the objects in such situations.
For reasons including the fact that conventional methods could not faithfully and easily generate 3D depth information, a need has arisen to propose a system and method of 3D depth generation that can recapture or generate 3D depth information to faithfully and easily recover or approximate a full 3D representation.